Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory278.0 B

Variable types

DateTime1
Text1
Numeric8
Categorical4

Alerts

Number_of_Claims has 3663 (36.6%) zeros Zeros

Reproduction

Analysis started2024-10-26 14:15:04.015827
Analysis finished2024-10-26 14:15:09.665480
Duration5.65 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

Date
Date

Distinct1096
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Minimum2020-01-01 00:00:00
Maximum2023-01-01 00:00:00
2024-10-26T19:45:09.728449image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:09.822529image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct500
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
2024-10-26T19:45:10.026818image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters90000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOL000353
2nd rowPOL000416
3rd rowPOL000200
4th rowPOL000109
5th rowPOL000327
ValueCountFrequency (%)
pol000323 36
 
0.4%
pol000077 34
 
0.3%
pol000245 32
 
0.3%
pol000005 32
 
0.3%
pol000336 31
 
0.3%
pol000471 31
 
0.3%
pol000034 31
 
0.3%
pol000084 31
 
0.3%
pol000010 30
 
0.3%
pol000078 30
 
0.3%
Other values (490) 9682
96.8%
2024-10-26T19:45:10.298775image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 34041
37.8%
P 10000
 
11.1%
O 10000
 
11.1%
L 10000
 
11.1%
4 4053
 
4.5%
3 3971
 
4.4%
1 3970
 
4.4%
2 3946
 
4.4%
6 2066
 
2.3%
5 2010
 
2.2%
Other values (3) 5943
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34041
37.8%
P 10000
 
11.1%
O 10000
 
11.1%
L 10000
 
11.1%
4 4053
 
4.5%
3 3971
 
4.4%
1 3970
 
4.4%
2 3946
 
4.4%
6 2066
 
2.3%
5 2010
 
2.2%
Other values (3) 5943
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34041
37.8%
P 10000
 
11.1%
O 10000
 
11.1%
L 10000
 
11.1%
4 4053
 
4.5%
3 3971
 
4.4%
1 3970
 
4.4%
2 3946
 
4.4%
6 2066
 
2.3%
5 2010
 
2.2%
Other values (3) 5943
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34041
37.8%
P 10000
 
11.1%
O 10000
 
11.1%
L 10000
 
11.1%
4 4053
 
4.5%
3 3971
 
4.4%
1 3970
 
4.4%
2 3946
 
4.4%
6 2066
 
2.3%
5 2010
 
2.2%
Other values (3) 5943
 
6.6%

Premium_Amount
Real number (ℝ)

Distinct9125
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean498.4197
Minimum-57.18
Maximum1045.76
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size78.3 KiB
2024-10-26T19:45:10.410929image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-57.18
5-th percentile251.8945
Q1397.815
median498.41
Q3598.2675
95-th percentile744.6115
Maximum1045.76
Range1102.94
Interquartile range (IQR)200.4525

Descriptive statistics

Standard deviation149.38995
Coefficient of variation (CV)0.29972721
Kurtosis-0.052908867
Mean498.4197
Median Absolute Deviation (MAD)100.325
Skewness0.028886735
Sum4984197
Variance22317.356
MonotonicityNot monotonic
2024-10-26T19:45:10.520536image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
483.63 4
 
< 0.1%
541.12 4
 
< 0.1%
505.3 4
 
< 0.1%
511.09 4
 
< 0.1%
469.06 4
 
< 0.1%
634.71 4
 
< 0.1%
457.14 4
 
< 0.1%
531.17 4
 
< 0.1%
610.19 3
 
< 0.1%
507.73 3
 
< 0.1%
Other values (9115) 9962
99.6%
ValueCountFrequency (%)
-57.18 1
< 0.1%
-0.49 1
< 0.1%
21.43 1
< 0.1%
24.55 1
< 0.1%
27.89 1
< 0.1%
35.55 1
< 0.1%
46.83 1
< 0.1%
53.78 1
< 0.1%
58.57 1
< 0.1%
58.58 1
< 0.1%
ValueCountFrequency (%)
1045.76 1
< 0.1%
1025.58 1
< 0.1%
1020.44 1
< 0.1%
1006.21 1
< 0.1%
1004.82 1
< 0.1%
982.37 1
< 0.1%
976.51 1
< 0.1%
971.6 1
< 0.1%
970.78 1
< 0.1%
968.33 1
< 0.1%

Claim_Amount
Real number (ℝ)

Distinct8918
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.17143
Minimum0.05
Maximum2399.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-10-26T19:45:10.625083image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile10.8795
Q157.8725
median141.2
Q3276.555
95-th percentile605.889
Maximum2399.2
Range2399.15
Interquartile range (IQR)218.6825

Descriptive statistics

Standard deviation199.29359
Coefficient of variation (CV)0.99066548
Kurtosis5.7721976
Mean201.17143
Median Absolute Deviation (MAD)97.205
Skewness1.9306526
Sum2011714.2
Variance39717.933
MonotonicityNot monotonic
2024-10-26T19:45:10.719486image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.16 4
 
< 0.1%
22.72 4
 
< 0.1%
44.43 4
 
< 0.1%
215.81 4
 
< 0.1%
34.32 3
 
< 0.1%
18.61 3
 
< 0.1%
50.56 3
 
< 0.1%
16.29 3
 
< 0.1%
12.16 3
 
< 0.1%
6.36 3
 
< 0.1%
Other values (8908) 9966
99.7%
ValueCountFrequency (%)
0.05 1
 
< 0.1%
0.08 2
< 0.1%
0.16 4
< 0.1%
0.17 3
< 0.1%
0.2 1
 
< 0.1%
0.22 1
 
< 0.1%
0.23 1
 
< 0.1%
0.26 1
 
< 0.1%
0.27 1
 
< 0.1%
0.29 1
 
< 0.1%
ValueCountFrequency (%)
2399.2 1
< 0.1%
1637.57 1
< 0.1%
1631.09 1
< 0.1%
1499.82 1
< 0.1%
1456.28 1
< 0.1%
1371.51 1
< 0.1%
1355.19 1
< 0.1%
1349.56 1
< 0.1%
1338.12 1
< 0.1%
1333.63 1
< 0.1%

Number_of_Claims
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0002
Minimum0
Maximum7
Zeros3663
Zeros (%)36.6%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-10-26T19:45:10.823827image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0010495
Coefficient of variation (CV)1.0008494
Kurtosis1.0473046
Mean1.0002
Median Absolute Deviation (MAD)1
Skewness1.0196917
Sum10002
Variance1.0021002
MonotonicityNot monotonic
2024-10-26T19:45:10.907253image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 3716
37.2%
0 3663
36.6%
2 1828
18.3%
3 588
 
5.9%
4 163
 
1.6%
5 39
 
0.4%
6 2
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 3663
36.6%
1 3716
37.2%
2 1828
18.3%
3 588
 
5.9%
4 163
 
1.6%
5 39
 
0.4%
6 2
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 2
 
< 0.1%
5 39
 
0.4%
4 163
 
1.6%
3 588
 
5.9%
2 1828
18.3%
1 3716
37.2%
0 3663
36.6%

Policy_Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size524.2 KiB
Life
3384 
Health
3318 
Auto
3298 

Length

Max length6
Median length4
Mean length4.6636
Min length4

Characters and Unicode

Total characters46636
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHealth
2nd rowAuto
3rd rowLife
4th rowAuto
5th rowAuto

Common Values

ValueCountFrequency (%)
Life 3384
33.8%
Health 3318
33.2%
Auto 3298
33.0%

Length

2024-10-26T19:45:11.003154image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T19:45:11.092859image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
life 3384
33.8%
health 3318
33.2%
auto 3298
33.0%

Most occurring characters

ValueCountFrequency (%)
e 6702
14.4%
t 6616
14.2%
L 3384
7.3%
i 3384
7.3%
f 3384
7.3%
H 3318
7.1%
a 3318
7.1%
l 3318
7.1%
h 3318
7.1%
A 3298
7.1%
Other values (2) 6596
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46636
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6702
14.4%
t 6616
14.2%
L 3384
7.3%
i 3384
7.3%
f 3384
7.3%
H 3318
7.1%
a 3318
7.1%
l 3318
7.1%
h 3318
7.1%
A 3298
7.1%
Other values (2) 6596
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46636
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6702
14.4%
t 6616
14.2%
L 3384
7.3%
i 3384
7.3%
f 3384
7.3%
H 3318
7.1%
a 3318
7.1%
l 3318
7.1%
h 3318
7.1%
A 3298
7.1%
Other values (2) 6596
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46636
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6702
14.4%
t 6616
14.2%
L 3384
7.3%
i 3384
7.3%
f 3384
7.3%
H 3318
7.1%
a 3318
7.1%
l 3318
7.1%
h 3318
7.1%
A 3298
7.1%
Other values (2) 6596
14.1%

Policy_Status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size550.3 KiB
Expired
3382 
Cancelled
3321 
Active
3297 

Length

Max length9
Median length7
Mean length7.3345
Min length6

Characters and Unicode

Total characters73345
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExpired
2nd rowExpired
3rd rowCancelled
4th rowCancelled
5th rowCancelled

Common Values

ValueCountFrequency (%)
Expired 3382
33.8%
Cancelled 3321
33.2%
Active 3297
33.0%

Length

2024-10-26T19:45:11.178595image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T19:45:11.242253image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
expired 3382
33.8%
cancelled 3321
33.2%
active 3297
33.0%

Most occurring characters

ValueCountFrequency (%)
e 13321
18.2%
d 6703
9.1%
i 6679
9.1%
l 6642
9.1%
c 6618
9.0%
E 3382
 
4.6%
x 3382
 
4.6%
p 3382
 
4.6%
r 3382
 
4.6%
C 3321
 
4.5%
Other values (5) 16533
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73345
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13321
18.2%
d 6703
9.1%
i 6679
9.1%
l 6642
9.1%
c 6618
9.0%
E 3382
 
4.6%
x 3382
 
4.6%
p 3382
 
4.6%
r 3382
 
4.6%
C 3321
 
4.5%
Other values (5) 16533
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73345
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13321
18.2%
d 6703
9.1%
i 6679
9.1%
l 6642
9.1%
c 6618
9.0%
E 3382
 
4.6%
x 3382
 
4.6%
p 3382
 
4.6%
r 3382
 
4.6%
C 3321
 
4.5%
Other values (5) 16533
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73345
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13321
18.2%
d 6703
9.1%
i 6679
9.1%
l 6642
9.1%
c 6618
9.0%
E 3382
 
4.6%
x 3382
 
4.6%
p 3382
 
4.6%
r 3382
 
4.6%
C 3321
 
4.5%
Other values (5) 16533
22.5%

Customer_Age
Real number (ℝ)

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.1496
Minimum18
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-10-26T19:45:11.336600image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q132
median46
Q360
95-th percentile72
Maximum74
Range56
Interquartile range (IQR)28

Descriptive statistics

Standard deviation16.51453
Coefficient of variation (CV)0.35784773
Kurtosis-1.2054265
Mean46.1496
Median Absolute Deviation (MAD)14
Skewness-0.0098044439
Sum461496
Variance272.72969
MonotonicityNot monotonic
2024-10-26T19:45:11.452018image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 201
 
2.0%
45 193
 
1.9%
67 192
 
1.9%
23 192
 
1.9%
34 191
 
1.9%
49 191
 
1.9%
65 191
 
1.9%
58 189
 
1.9%
30 188
 
1.9%
74 187
 
1.9%
Other values (47) 8085
80.8%
ValueCountFrequency (%)
18 187
1.9%
19 177
1.8%
20 168
1.7%
21 172
1.7%
22 169
1.7%
23 192
1.9%
24 149
1.5%
25 178
1.8%
26 180
1.8%
27 162
1.6%
ValueCountFrequency (%)
74 187
1.9%
73 182
1.8%
72 175
1.8%
71 182
1.8%
70 182
1.8%
69 173
1.7%
68 175
1.8%
67 192
1.9%
66 172
1.7%
65 191
1.9%

Customer_Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size527.4 KiB
Male
5043 
Female
4957 

Length

Max length6
Median length4
Mean length4.9914
Min length4

Characters and Unicode

Total characters49914
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 5043
50.4%
Female 4957
49.6%

Length

2024-10-26T19:45:11.561140image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T19:45:11.644595image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
male 5043
50.4%
female 4957
49.6%

Most occurring characters

ValueCountFrequency (%)
e 14957
30.0%
a 10000
20.0%
l 10000
20.0%
M 5043
 
10.1%
F 4957
 
9.9%
m 4957
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49914
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14957
30.0%
a 10000
20.0%
l 10000
20.0%
M 5043
 
10.1%
F 4957
 
9.9%
m 4957
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49914
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14957
30.0%
a 10000
20.0%
l 10000
20.0%
M 5043
 
10.1%
F 4957
 
9.9%
m 4957
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49914
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14957
30.0%
a 10000
20.0%
l 10000
20.0%
M 5043
 
10.1%
F 4957
 
9.9%
m 4957
 
9.9%

Renewal_Probability
Real number (ℝ)

Distinct101
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.498111
Minimum0
Maximum1
Zeros52
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-10-26T19:45:11.731814image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.25
median0.5
Q30.75
95-th percentile0.95
Maximum1
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.28816014
Coefficient of variation (CV)0.57850587
Kurtosis-1.1950532
Mean0.498111
Median Absolute Deviation (MAD)0.25
Skewness0.0066087112
Sum4981.11
Variance0.083036265
MonotonicityNot monotonic
2024-10-26T19:45:11.841307image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.85 123
 
1.2%
0.39 121
 
1.2%
0.26 118
 
1.2%
0.4 117
 
1.2%
0.6 116
 
1.2%
0.18 116
 
1.2%
0.55 115
 
1.1%
0.83 113
 
1.1%
0.05 113
 
1.1%
0.81 113
 
1.1%
Other values (91) 8835
88.3%
ValueCountFrequency (%)
0 52
0.5%
0.01 96
1.0%
0.02 92
0.9%
0.03 110
1.1%
0.04 87
0.9%
0.05 113
1.1%
0.06 106
1.1%
0.07 110
1.1%
0.08 106
1.1%
0.09 97
1.0%
ValueCountFrequency (%)
1 58
0.6%
0.99 98
1.0%
0.98 98
1.0%
0.97 101
1.0%
0.96 106
1.1%
0.95 80
0.8%
0.94 85
0.9%
0.93 92
0.9%
0.92 101
1.0%
0.91 85
0.9%

Risk_Score
Real number (ℝ)

Distinct101
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.999
Minimum0
Maximum100
Zeros100
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-10-26T19:45:11.935674image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q125
median50
Q375.25
95-th percentile96
Maximum100
Range100
Interquartile range (IQR)50.25

Descriptive statistics

Standard deviation29.205717
Coefficient of variation (CV)0.58412602
Kurtosis-1.2028252
Mean49.999
Median Absolute Deviation (MAD)25
Skewness-0.003790661
Sum499990
Variance852.9739
MonotonicityNot monotonic
2024-10-26T19:45:12.066387image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 121
 
1.2%
87 119
 
1.2%
55 118
 
1.2%
10 118
 
1.2%
51 117
 
1.2%
79 116
 
1.2%
27 115
 
1.1%
80 114
 
1.1%
97 114
 
1.1%
28 111
 
1.1%
Other values (91) 8837
88.4%
ValueCountFrequency (%)
0 100
1.0%
1 106
1.1%
2 86
0.9%
3 103
1.0%
4 104
1.0%
5 103
1.0%
6 101
1.0%
7 100
1.0%
8 96
1.0%
9 96
1.0%
ValueCountFrequency (%)
100 86
0.9%
99 97
1.0%
98 106
1.1%
97 114
1.1%
96 99
1.0%
95 106
1.1%
94 91
0.9%
93 103
1.0%
92 101
1.0%
91 82
0.8%

Customer_Loyalty_Score
Real number (ℝ)

Distinct101
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.1126
Minimum0
Maximum100
Zeros82
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-10-26T19:45:12.177275image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q125
median50
Q375
95-th percentile95
Maximum100
Range100
Interquartile range (IQR)50

Descriptive statistics

Standard deviation29.030363
Coefficient of variation (CV)0.57930268
Kurtosis-1.1927099
Mean50.1126
Median Absolute Deviation (MAD)25
Skewness0.0057560895
Sum501126
Variance842.762
MonotonicityNot monotonic
2024-10-26T19:45:12.285119image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 133
 
1.3%
99 127
 
1.3%
47 120
 
1.2%
26 119
 
1.2%
93 118
 
1.2%
34 117
 
1.2%
83 115
 
1.1%
4 113
 
1.1%
12 113
 
1.1%
15 113
 
1.1%
Other values (91) 8812
88.1%
ValueCountFrequency (%)
0 82
0.8%
1 82
0.8%
2 83
0.8%
3 107
1.1%
4 113
1.1%
5 101
1.0%
6 105
1.1%
7 98
1.0%
8 89
0.9%
9 104
1.0%
ValueCountFrequency (%)
100 95
0.9%
99 127
1.3%
98 96
1.0%
97 89
0.9%
96 91
0.9%
95 97
1.0%
94 102
1.0%
93 118
1.2%
92 101
1.0%
91 87
0.9%

Year
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size517.7 KiB
2022
3358 
2021
3340 
2020
3296 
2023
 
6

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters40000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2022 3358
33.6%
2021 3340
33.4%
2020 3296
33.0%
2023 6
 
0.1%

Length

2024-10-26T19:45:12.369996image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T19:45:12.448700image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2022 3358
33.6%
2021 3340
33.4%
2020 3296
33.0%
2023 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2 23358
58.4%
0 13296
33.2%
1 3340
 
8.3%
3 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 23358
58.4%
0 13296
33.2%
1 3340
 
8.3%
3 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 23358
58.4%
0 13296
33.2%
1 3340
 
8.3%
3 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 23358
58.4%
0 13296
33.2%
1 3340
 
8.3%
3 6
 
< 0.1%

Month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5615
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-10-26T19:45:12.526763image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4747986
Coefficient of variation (CV)0.52957381
Kurtosis-1.2224473
Mean6.5615
Median Absolute Deviation (MAD)3
Skewness-0.029600456
Sum65615
Variance12.074225
MonotonicityNot monotonic
2024-10-26T19:45:12.605413image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 887
8.9%
1 885
8.8%
7 885
8.8%
12 869
8.7%
3 864
8.6%
11 859
8.6%
8 828
8.3%
5 819
8.2%
4 796
8.0%
9 791
7.9%
Other values (2) 1517
15.2%
ValueCountFrequency (%)
1 885
8.8%
2 732
7.3%
3 864
8.6%
4 796
8.0%
5 819
8.2%
6 785
7.8%
7 885
8.8%
8 828
8.3%
9 791
7.9%
10 887
8.9%
ValueCountFrequency (%)
12 869
8.7%
11 859
8.6%
10 887
8.9%
9 791
7.9%
8 828
8.3%
7 885
8.8%
6 785
7.8%
5 819
8.2%
4 796
8.0%
3 864
8.6%

Interactions

2024-10-26T19:45:08.709130image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:04.470005image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:05.113841image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:05.705926image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:06.308631image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:06.988845image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:07.586181image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:08.146187image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:08.772917image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:04.542402image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:05.186095image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:05.781326image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:06.387456image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:07.058558image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:07.659850image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:08.209269image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:08.837937image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:04.609373image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:05.264824image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:05.855684image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:06.462378image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:07.128248image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:07.725441image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:08.289286image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:08.921684image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:04.680652image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:05.345264image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:05.931422image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:06.529588image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:07.204145image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:07.802939image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:08.367043image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:08.993045image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:04.823820image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:05.420604image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:06.002079image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:06.613979image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:07.273697image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:07.872381image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:08.433792image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:09.060458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:04.902060image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:05.490191image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:06.076983image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:06.687301image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:07.343002image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:07.941778image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:08.501920image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:09.122927image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:04.965367image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:05.560571image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:06.151873image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:06.753321image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:07.419879image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:08.013460image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:08.574647image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:09.185963image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:05.034913image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:05.630026image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:06.226704image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:06.921669image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:07.510609image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:08.091932image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-26T19:45:08.641967image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-10-26T19:45:12.822144image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Claim_AmountCustomer_AgeCustomer_GenderCustomer_Loyalty_ScoreMonthNumber_of_ClaimsPolicy_StatusPolicy_TypePremium_AmountRenewal_ProbabilityRisk_ScoreYear
Claim_Amount1.0000.0010.0000.012-0.0020.0020.0080.007-0.013-0.0090.0010.005
Customer_Age0.0011.0000.0160.0010.003-0.0050.0010.034-0.0140.0010.0010.000
Customer_Gender0.0000.0161.0000.0000.0180.0000.0120.0060.0170.0000.0240.010
Customer_Loyalty_Score0.0120.0010.0001.000-0.0090.0050.0040.000-0.0180.0080.0110.006
Month-0.0020.0030.018-0.0091.0000.0070.0000.0160.011-0.005-0.0100.020
Number_of_Claims0.002-0.0050.0000.0050.0071.0000.0170.0070.002-0.001-0.0100.000
Policy_Status0.0080.0010.0120.0040.0000.0171.0000.0000.0200.0050.0200.010
Policy_Type0.0070.0340.0060.0000.0160.0070.0001.0000.0040.0000.0200.000
Premium_Amount-0.013-0.0140.017-0.0180.0110.0020.0200.0041.0000.010-0.0130.019
Renewal_Probability-0.0090.0010.0000.008-0.005-0.0010.0050.0000.0101.0000.0100.012
Risk_Score0.0010.0010.0240.011-0.010-0.0100.0200.020-0.0130.0101.0000.000
Year0.0050.0000.0100.0060.0200.0000.0100.0000.0190.0120.0001.000

Missing values

2024-10-26T19:45:09.390322image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-26T19:45:09.582659image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DatePolicy_IDPremium_AmountClaim_AmountNumber_of_ClaimsPolicy_TypePolicy_StatusCustomer_AgeCustomer_GenderRenewal_ProbabilityRisk_ScoreCustomer_Loyalty_ScoreYearMonth
02020-01-01POL000353358.29271.212HealthExpired19Male0.1496920201
12020-01-01POL000416243.7814.702AutoExpired33Female0.93548520201
22020-01-01POL000200301.7470.460LifeCancelled68Male0.19453720201
32020-01-01POL000109491.45424.611AutoCancelled57Male0.35921920201
42020-01-01POL000327429.26420.430AutoCancelled43Female0.0879920201
52020-01-01POL000234654.58154.802LifeActive32Male0.89182620201
62020-01-01POL000403684.50619.110AutoExpired67Male0.97129520201
72020-01-01POL000431316.77228.560HealthCancelled64Female0.41495920201
82020-01-01POL000294522.08189.641LifeCancelled42Male0.12658020201
92020-01-01POL000362501.04162.311HealthActive67Female0.37482020201
DatePolicy_IDPremium_AmountClaim_AmountNumber_of_ClaimsPolicy_TypePolicy_StatusCustomer_AgeCustomer_GenderRenewal_ProbabilityRisk_ScoreCustomer_Loyalty_ScoreYearMonth
99902022-12-31POL000288511.60167.760AutoExpired69Male0.717785202212
99912022-12-31POL000233359.251115.800HealthActive56Male0.277947202212
99922022-12-31POL000381510.92142.360AutoActive68Male0.053433202212
99932022-12-31POL000061423.05835.561LifeActive43Male0.07104202212
99942023-01-01POL000349399.50604.100AutoCancelled58Male0.55477820231
99952023-01-01POL000274452.99169.043LifeActive20Male0.70258420231
99962023-01-01POL000162619.28315.721HealthCancelled47Male0.29467120231
99972023-01-01POL000247480.6820.671LifeExpired55Female0.631002220231
99982023-01-01POL000436342.79266.500AutoCancelled25Male0.786310020231
99992023-01-01POL000473440.4667.782AutoCancelled55Female0.7931320231